We present a method that expands on previous work in learning human perceived style similarity across objects with different
structures and functionalities. Unlike previous approaches that tackle this problem with the help of hand-crafted geometric
descriptors, we make use of recent advances in metric learning with neural networks (deep metric learning). This allows us to
train the similarity metric on a shape collection directly, since any low- or high-level features needed to discriminate between
different styles are identified by the neural network automatically. Furthermore, we avoid the issue of finding and comparing
sub-elements of the shapes. We represent the shapes as rendered images and show how image tuples can be selected, generated
and used efficiently for deep metric learning. We also tackle the problem of training our neural networks on relatively small
datasets and show that we achieve style classification accuracy competitive with the state of the art. Finally, to reduce annotation
effort we propose a method to incorporate heterogeneous data sources by adding annotated photos found online in order to
expand or supplant parts of our training data.